Input Color Research Articles

A perception-guided CNN for grape bunch detection

Precision Viticulture (PV) is becoming an active and interdisciplinary research field since it requires solving interesting research issues to concretely answer the demands of specific use cases. A challenging problem in this context is the development of automatic methods for yield estimation. Computer vision methods can contribute to the accomplishment of this task, especially those that can replicate what winemakers do manually. In this paper, an automatic artificial intelligence method for grape bunch detection from RGB images is presented. A customized Convolutional Neural Network (CNN) is employed for pointwise classification of image pixels and the dependence of classification results on the type of input color channels and grapes color properties are studied. The advantage of using additional perception-based input features, such as luminance and visual contrast, is also evaluated, as well as the dependence of the method on the choice of the training set in terms of the amount of labeled data. The latter point has a significant impact on the practical use of the method on-site, its usability by non-expert users, and its adaptability to individual vineyards. Experimental results show that a properly trained CNN can discriminate and detect grape bunches even under uncontrolled acquisition conditions and with limited computational load, making the proposed method implementable on smart devices and suitable for on-site and real-time applications.

Development of Digital Image Processing Algorithms via FPGA Implementation

Real-time image processing is one of the fundamental elements in achieving IR 4.0. The rapid development of digital image processing techniques has enabled various applications in fields such as healthcare, transportation, and manufacturing. People are seeking higher-performance image processing as traditional image processing is no longer fulfilling the demands. FPGA-based digital image processing has become one of the choices for the public due to its parallel pipelining, which enables shorter processing time and better performance. Several digital image processing algorithms have been developed in this project, which are gray level transformation, brightness manipulation, contrast adjustment, thresholding, and inversion. They are the most popular algorithms used in digital image processing. Microsoft Paint is used to convert the format of the color input image to bitmap format, followed by MATLAB to convert it into a hexadecimal file to be read and written in FPGA. Platforms such as ModelSim Altera and Intel Quartus II are used to write Verilog HDL for digital image processing algorithms. As a result, five hexadecimal files are obtained from the simulation. The output hexadecimal files are further processed in MATLAB to generate respective images.

Eye Deep-Net: a deep neural network-based multi-class retinal disease diagnostic

Ophthalmologists rely heavily on retinal pictures to diagnose a wide range of eye conditions. Numerous retinal disorders may lead to microvascular alterations in the retina, and a number of studies have been conducted on the early identification of medical pictures to enable prompt and appropriate treatment. In order to identify various eye illnesses using color fundus pictures, this study develops a non-invasive, automated deep learning system. A multiclass ocular illness A productive diagnostic approach was created using the Remind dataset. A variety of augmentation strategies were used to make the structure robust in real-time after multi-class fundus pictures were collected from a multi-label dataset. Low computational demand images were processed in accordance with the network. The fundamental convolutional neural network (CNN) extracts appropriate characteristics from the input color fundus image dataset, and then processed characteristics were employed to make predictive diagnoses. This multi-layer neural network, called Eye Deep-Net, has been developed for training and evaluating images for the recognition of various eye problems. The performance of the suggested model is determined to be much better than numerous baseline state-of-the-art models. The strength from the Eye Deep-Net is assessed using different statistical metrics. The suggested methodology's effectiveness in classifying and identifying diseases using digital fundus pictures is shown by a thorough comparison with the most modern techniques.

A Cascade U‐Net With Transformer for Retinal Multi‐Lesion Segmentation

ABSTRACTDiabetic retinopathy (DR) is an important cause of blindness. If not diagnosed and treated in a timely manner, it can lead to irreversible vision loss. The diagnosis of DR relies heavily on specialized ophthalmologists. In recent years, with the development of artificial intelligence a number of diagnostics using this technique have begun to appear. One method for diagnosing diseases in this field is to segment four common kinds of lesions from color fundus images, including: exudates (EX), soft exudates (SE), hemorrhages (HE), and microaneurysms (MA). In this paper, we propose a segmentation model for DR based on deep learning. The main part of the model consists of two layers of improved U‐Net network based on transformer, corresponding to the two stages of coarse segmentation and fine segmentation, respectively. The model can segment four common kinds of lesions from the input color fundus image at the same time. To validate the performance of our proposed model, we test our model on three public datasets: IDRiD, DDR, and DIARETDB1. The test results show that our proposed model achieves competitive results compared with the existing methods in terms of PR‐AUC, ROC‐AUC, Dice, and IoU, especially for lesions segmentation of SE and MA.

Bias-compensated based diffusion affine projection like maximum correntropy algorithm

A distributed network with highly colored input signal is often located in a scenario where the output signal is subject to impulse noise. Under the above scenario, the performance of the diffusion adaptive algorithms will suffer from performance degradation, which includes distributed parameter estimation accuracy and convergence/tracking rate. To solve these problems, the diffusion affine projection like maximum correntropy (DAPLMC) algorithm is proposed. Moreover, highly colored input signals tend to be mixed with noise, which will lead to biased estimation. To deal with the adverse impact of biased estimation, based on the bias compensation (BC) strategy, the bias-compensated DAPLMC (BC-DAPLMC) algorithm is proposed in this paper. By analyzing the convergence performance of the BC-DAPLMC algorithm, a range of its step size is obtained, and the steady-state error of the BC-DAPLMC algorithm is studied. The superior performance of the BC-DAPLMC algorithm and theoretical steady-state error analysis is validated by simulation results.

A non-contact colour correction system on mobile display under various light sources

Human recognize perceived color on the display device of a smart phone under various types of light sources and illuminance differently even though the display represents the same color. This study investigated the relationship between input RGB code and output color tristimulus (XYZ) on two types of smartphone displays under various types of light sources and illuminances. A non-contact color correction system to transfer similar color with actual object was then proposed under various types of light sources and illuminances. Furthermore, the color correction model was validated by input color RGB predicted for representing the desired color and a survey test on displays of two types of smartphone, iPhone 13 Pro and Galaxy S22.

Color-to-Gray Conversion Method Based on Chroma Difference

Most of the images encountered in daily life are color images, yet grayscale remains prevalent in many fields due to its reduced data size and simplified operations. When reducing the dimensions of a three-channel color image to a single-channel grayscale, a portion of the original color information is inevitably lost. To yield high-quality grayscale images, this study introduces a color-to-gray conversion method based on chroma difference. This method defines a novel color distance metric for color-to-grayscale conversion, incorporating pixel chromaticity differences alongside brightness variations. The discrepancy in gray pixel values in the output grayscale image effectively mirrors the overall differences among input color image pixels. Optimization is conducted using the conjugate gradient method, ensuring appropriate reflection of luminance information from the input image and chromaticity data from the original color image within the grayscale rendering. Experimental validation confirms the efficacy of this approach. However, as the method accounts for all pixel pairs, it occasionally considers unnecessary pairs, leading to potential distortion in color differences between pixels and consequent inadequacies in chromaticity variation. To address this issue, a weighting factor is introduced, prioritizing color combinations with similar color differences. Experimental findings demonstrate that grayscale images produced using the proposed method outperform those generated by alternative approaches in preserving color differentials and retaining detailed features of the original image. The resulting output exhibits clear and natural outlines, as supported by both subjective and objective assessments.

Optimizing support vector machine (SVM) by social spider optimization (SSO) for edge detection in colored images

Edge detection in images is a vital application of image processing in fields such as object detection and identification of lesion regions in medical images. This problem is more complex in the domain of color images due to the combination of color layer information and the need to achieve a unified edge boundary across these layers, which increases the complexity of the problem. In this paper, a simple and effective method for edge detection in color images is proposed using a combination of support vector machine (SVM) and the social spider optimization (SSO) algorithm. In the proposed method, the input color image is first converted to a grayscale image, and an initial estimation of the image edges is performed based on it. To this end, the proposed method utilizes an SVM with a Radial Basis Function (RBF) kernel, in which the model's hyperparameters are tuned using the SSO algorithm. After the formation of initial image edges, the resulting edges are compared with pairwise combinations of color layers, and an attempt is made to improve the edge localization using the SSO algorithm. In this step, the optimization algorithm's task is to refine the image edges in a way that maximizes the compatibility with pairwise combinations of color layers. This process leads to the formation of prominent image edges and reduces the adverse effects of noise on the final result. The performance of the proposed method in edge detection of various color images has been evaluated and compared with similar previous strategies. According to the obtained results, the proposed method can successfully identify image edges more accurately, as the edges identified by the proposed method have an average accuracy of 93.11% for the BSDS500 database, which is an increase of at least 0.74% compared to other methods.

A deep learning framework for the early detection of multi-retinal diseases.

Retinal images play a pivotal contribution to the diagnosis of various ocular conditions by ophthalmologists. Extensive research was conducted to enable early detection and timely treatment using deep learning algorithms for retinal fundus images. Quick diagnosis and treatment planning can be facilitated by deep learning models' ability to process images rapidly and deliver outcomes instantly. Our research aims to provide a non-invasive method for early detection and timely eye disease treatment using a Convolutional Neural Network (CNN). We used a dataset Retinal Fundus Multi-disease Image Dataset (RFMiD), which contains various categories of fundus images representing different eye diseases, including Media Haze (MH), Optic Disc Cupping (ODC), Diabetic Retinopathy (DR), and healthy images (WNL). Several pre-processing techniques were applied to improve the model's performance, such as data augmentation, cropping, resizing, dataset splitting, converting images to arrays, and one-hot encoding. CNNs have extracted extract pertinent features from the input color fundus images. These extracted features are employed to make predictive diagnostic decisions. In this article three CNN models were used to perform experiments. The model's performance is assessed utilizing statistical metrics such as accuracy, F1 score, recall, and precision. Based on the results, the developed framework demonstrates promising performance with accuracy rates of up to 89.81% for validation and 88.72% for testing using 12-layer CNN after Data Augmentation. The accuracy rate obtained from 20-layer CNN is 90.34% for validation and 89.59% for testing with Augmented data. The accuracy obtained from 20-layer CNN is greater but this model shows overfitting. These accuracy rates suggested that the deep learning model has learned to distinguish between different eye disease categories and healthy images effectively. This study's contribution lies in providing a reliable and efficient diagnostic system for the simultaneous detection of multiple eye diseases through the analysis of color fundus images.

Text Localization and Enhancement of Mobile Camera based Complex Natural Bilingual Text Scene Images

In this paper, we proposed a text localization and enhancement of the Mobile camera based complex natural scene images using fixed coordinates for removal of unwanted non text regions and making it localized. The morphological erosion technique is applied for the smoothness of foreground text regions. Here, the experimental working nature of the proposed method is first localized color input image is converted into a gray level, then a median filter is applied for the removal of noises. After that, the input image is further converted into binary image. In addition to this, morphological dilation and erosion operations also applied for connecting the broken pixels. Further, the connected component segmentation technique is applied to extract the foreground text regions from the binary input image. The real time input samples are collected by using mobile camera from the various categories of natural scenes of monolingual/bilingual text such as Stone, Wall, Poster and Iron-based. A total of 140 real time samples of natural scene text images are considered for the experimental setup. The experimental results shown that 132 real time sample images are localized and then obtained the accuracy of 94.24%. Further, bilingual text regions are enhanced using hybrid approach and obtain the segmentation accuracy of 83.17%. The novelty of the paper is working on the real time data set and it contains a mixture of printed and handwritten natural scene text images.

Utility of Spectral Filtering to Improve the Reliability of Marine Fauna Detections from Drone-Based Monitoring.

Monitoring marine fauna is essential for mitigating the effects of disturbances in the marine environment, as well as reducing the risk of negative interactions between humans and marine life. Drone-based aerial surveys have become popular for detecting and estimating the abundance of large marine fauna. However, sightability errors, which affect detection reliability, are still apparent. This study tested the utility of spectral filtering for improving the reliability of marine fauna detections from drone-based monitoring. A series of drone-based survey flights were conducted using three identical RGB (red-green-blue channel) cameras with treatments: (i) control (RGB), (ii) spectrally filtered with a narrow 'green' bandpass filter (transmission between 525 and 550 nm), and, (iii) spectrally filtered with a polarising filter. Video data from nine flights comprising dolphin groups were analysed using a machine learning approach, whereby ground-truth detections were manually created and compared to AI-generated detections. The results showed that spectral filtering decreased the reliability of detecting submerged fauna compared to standard unfiltered RGB cameras. Although the majority of visible contrast between a submerged marine animal and surrounding seawater (in our study, sites along coastal beaches in eastern Australia) is known to occur between 515-554 nm, isolating the colour input to an RGB sensor does not improve detection reliability due to a decrease in the signal to noise ratio, which affects the reliability of detections.

Differential encoding of temporally evolving color patterns across nearby V1 neurons.

Whereas studies of the V1 cortex have focused mainly on neural line orientation preference, color inputs are also known to have a strong presence among these neurons. Individual neurons typically respond to multiple colors and nearby neurons have different combinations of preferred color inputs. However, the computations performed by V1 neurons on such color inputs have not been extensively studied. Here we aimed to address this issue by studying how different V1 neurons encode different combinations of inputs composed of four basic colors. We quantified the decoding accuracy of individual neurons from multi-electrode array recordings, comparing multiple individual neurons located within 2 mm along the vertical axis of the V1 cortex of the anesthetized rat. We found essentially all V1 neurons to be good at decoding spatiotemporal patterns of color inputs and they did so by encoding them in different ways. Quantitative analysis showed that even adjacent neurons encoded the specific input patterns differently, suggesting a local cortical circuitry organization which tends to diversify rather than unify the neuronal responses to each given input. Using different pairs of monocolor inputs, we also found that V1 neocortical neurons had a diversified and rich color opponency across the four colors, which was somewhat surprising given the fact that rodent retina express only two different types of opsins. We propose that the processing of color inputs in V1 cortex is extensively composed of multiple independent circuitry components that reflect abstract functionalities resident in the internal cortical processing rather than the raw sensory information per se.

Depth super-resolution from explicit and implicit high-frequency features

Guided depth super-resolution aims at using a low-resolution depth map and an associated high-resolution RGB image to recover a high-resolution depth map. However, restoring precise and sharp edges near depth discontinuities and fine structures is still challenging for state-of-the-art methods. To alleviate this issue, we propose a novel multi-stage depth super-resolution network, which progressively reconstructs HR depth maps from explicit and implicit high-frequency information. We introduce an efficient transformer to obtain explicit high-frequency information. The shape bias and global context of the transformer allow our model to focus on high-frequency details between objects, i.e., depth discontinuities, rather than texture within objects. Furthermore, we project the input color images into the frequency domain for additional implicit high-frequency cues extraction. Finally, to incorporate the structural details, we develop a fusion strategy that combines depth features and high-frequency information in the multi-stage-scale framework. Exhaustive experiments on the main benchmarks show that our approach establishes a new state-of-the-art. Code will be publicly available at https://github.com/wudiqx106/DSR-EI.

Robust multitask diffusion normalized M-estimate subband adaptive filter algorithm over adaptive networks

In recent years, the multitask diffusion least mean square (MD-LMS) algorithm has been extensively applied in distributed parameter estimation and target tracking of multitask network. However, its performance degrades under colored input signals or impulsive noises. To overcome these two drawbacks, this paper introduces the subband adaptive filter (SAF) into a multitask network for the first time, and a robust multitask diffusion normalized M-estimate subband adaptive filtering (MD-NMSAF) algorithm is proposed, which solves the global network optimization problem based on the modified Huber (MH) function in a distributed manner, achieves robustness to impulsive noise, and significantly improves the convergence performance of the MD-LMS algorithm. Compared with existing robust multitask diffusion affine projection algorithms (MD-APAs), the computational complexity of the proposed MD-NMSAF algorithm is greatly reduced. In addition, we analyze the mean and mean-square stability conditions of MD-NMSAF, provide theoretical models characterizing the network mean square deviation (MSD) behaviors of transient and steady-state, and further verify their correctness by computer simulations. Simulation results under different network topologies, input signals, filter lengths and impulsive noise types fully demonstrate the performance advantages of the proposed MD-NMSAF algorithm over its competitors in terms of steady-state estimation accuracy and convergence speed.

Block-Adaptive Point Cloud Attribute Coding With Region-Aware Optimized Transform

Block-based compression scheme shows remarkable success in image and video coding. However, existing tree-type block partition methods usually divide point clouds into clusters with few or disjoint points due to the irregular sampling, which is adverse for the subsequent transform to exploit the local region correlation. Moreover, the widely-used optimal transform, e.g., Discrete Cosine Transform (DCT), is deduced with the assumption of the specified probability model. Thus these transforms cannot adequately conform to diverse statistical characteristics of point cloud attributes. To address the above two problems, we propose a block-adaptive codec with the optimized transform for point cloud attribute compression, including Progressive Clustering (PC) for block partition and Region-Aware Signal Modeling (RASM) for transform. PC is designed via the split-and-merge strategy. In the splitting phase, over-segmented clusters are obtained by iteratively searching the nearest neighbors to maintain the spatial continuity of points in one cluster. In the merging step, blocks are determined by merging over-segmented clusters under the guidance of minimizing texture complexity, which adjusts block sizes to adapt to texture variation. RASM adaptively captures the color correlations to respond to diverse statistical characteristics by optimizing the overall Rate-Distortion (RD) cost. Then, the optimal transform bases are obtained via the eigen-decomposition of the color correlation representation with respect to the Gaussian Markov Random Field, where input colors are obtained considering the linear relationship between geometry and color variations. The linear coefficients also need to be encoded to combine geometry to reconstruct the transform bases at the decoder in the same way as the encoder. Particularly, we accelerate the RASM by constraining the independent relationship between signals in the local region, which hardly influences RD performance. Extensive experiments not only indicate the effectiveness of PC and RASM but also show the significant RD gains achieved by our approach compared with several state-of-the-art platforms.

Underwater Image Color Constancy Calculation with Optimized Deep Extreme Learning Machine Based on Improved Arithmetic Optimization Algorithm

To overcome the challenges posed by the underwater environment and restore the true colors of marine objects’ surfaces, a novel underwater image illumination estimation model, termed the iterative chaotic improved arithmetic optimization algorithm for deep extreme learning machines (IAOA-DELM), is proposed. In this study, the gray edge framework is utilized to extract color features from underwater images, which are employed as input vectors. To address the issue of unstable prediction results caused by the random selection of parameters in DELM, the arithmetic optimization algorithm (AOA) is integrated, and the search segment mapping method is optimized by using hidden layer biases and input layer weights. Furthermore, an iterative chaotic mapping initialization strategy is incorporated to provide AOA with a better initial search proxy. The IAOA-DELM model computes illumination information based on the input color vectors. Experimental evaluations conducted on actual underwater images demonstrate that the proposed IAOA-DELM illumination correction model achieves an accuracy of 96.07%. When compared to the ORELM, ELM, RVFL, and BP models, the IAOA-DELM model exhibits improvements of 6.96%, 7.54%, 8.00%, and 8.89%, respectively, making it the most effective among the compared illumination correction models.

A Novel NLMS Algorithm for System Identification

In this paper, we propose a novel normalized least mean squares (NLMS) algorithm for system identification applications. Our approach involves analyzing the mean squared deviation performance of the NLMS algorithm using a random walk model to select two optimal parameters, the step size and regularization parameters, for the rapid convergence of the colored input signals. We verified that the proposed algorithm exhibited faster convergence than existing algorithms, even in scenarios of sudden system changes.

One‐to‐Many Spectral Upsampling of Reflectances and Transmittances

AbstractSpectral rendering is essential for the production of physically‐plausible synthetic images, but requires to introduce several changes in the content generation pipeline. In particular, the authoring of spectral material properties (e.g., albedo maps, indices of refraction, transmittance coefficients) raises new problems. While a large panel of computer graphics methods exists to upsample a RGB color to a spectrum, they all provide a one‐to‐one mapping. This limits the ability to control interesting color changes such as the Usambara effect or metameric spectra. In this work, we introduce a one‐to‐many mapping in which we show how we can explore the set of all spectra reproducing a given input color. We apply this method to different colour changing effects such as vathochromism – the change of color with depth, and metamerism.

Improving the planarity and sharpness of monocularly estimated depth images using the Phong reflection model

Depth estimation based on a single RGB image (a.k.a. monocular depth estimation) is a challenging task, with applications in robotics, autonomous vehicles, and other areas. With the advance of deep learning, several monocular depth estimation approaches have emerged with remarkable results. However, it is still possible to observe deficiencies in the depth maps generated by current techniques. We present a deep neural network that takes the result produced by some existing monocular depth estimation approach and enhances it by adding the details that the depth map requires to be sharp. We train the proposed Depth Enhancer Neural Network (DENN) using a new loss function that compares the input color image of the scene to the color image produced by rendering it using the Phong reflection model. Our experiments show a clear visual improvement in the sharpness of the depth image produced by the DENN, leading to edge enhancement and regularity of planar surfaces without compromising non-planar objects. On average, the standard metrics show a 5% reduction in error when considering the quantitative results. This reduction is dependent on the initial monocular depth estimation technique used by DENN to train the model.

A DNA Based Colour Image Encryption Scheme Using A Convolutional Autoencoder

With the advancement in technology, digital images can easily be transmitted and stored over the Internet. Encryption is used to avoid illegal interception of digital images. Encrypting large-sized colour images in their original dimension generally results in low encryption/decryption speed along with exerting a burden on the limited bandwidth of the transmission channel. To address the aforementioned issues, a new encryption scheme for colour images employing convolutional autoencoder, DNA and chaos is presented in this paper. The proposed scheme has two main modules, the dimensionality conversion module using the proposed convolutional autoencoder, and the encryption/decryption module using DNA and chaos. The dimension of the input colour image is first reduced from N × M × 3 to P × Q gray-scale image using the encoder. Encryption and decryption are then performed in the reduced dimension space. The decrypted gray-scale image is upsampled to obtain the original colour image having dimension N × M × 3 . The training and validation accuracy of the proposed autoencoder is 97% and 95%, respectively. Once the autoencoder is trained, it can be used to reduce and subsequently increase the dimension of any arbitrary input colour image. The efficacy of the designed autoencoder has been demonstrated by the successful reconstruction of the compressed image into the original colour image with negligible perceptual distortion. The second major contribution presented in this paper is an image encryption scheme using DNA along with multiple chaotic sequences and substitution boxes. The security of the proposed image encryption algorithm has been gauged using several evaluation parameters, such as histogram of the cipher image, entropy, NPCR, UACI, key sensitivity, contrast, and so on. The experimental results of the proposed scheme demonstrate its effectiveness to perform colour image encryption.