False Contour Research Articles

Bit depth enhancement method based on visual contrast perception features

High-bit images can show more detail than low-bit images do. False contours in the low-bit images should be suppressed when converting low-bit images to high-bit ones. In this paper, a contrast-aware bit depth enhancement (BDE) module is proposed based on the visual perceptual feature which models the relationship of contrast sensitivity and image bit depth. And a bit depth enhancement network is constructed by the cascade of this module. The experimental results show that the bit depth enhancement algorithm based on this network structure has the best or second-best peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) metrics when compared with the related algorithms, and on average, our PSNR (40.20 dB) is only 1.03% and SSIM (0.9681) is only 0.11% lower than that of the state-of-the-arts, but uses 6.7% fewer parameters (11.2M). And visual comparisons show that our algorithm can effectively suppress false contours and color distortions, resulting in high-bit images with better quality.

MRFT: Multiscale Recurrent Fusion Transformer Based Prior Knowledge for Bit-Depth Enhancement

Bit-depth enhancement (BDE) plays an important role in providing high bit-depth data support for high-dynamic range (HDR) display. Although convolutional neural network (CNN) based BDE methods have achieved top performance, multiscale feature extraction and fusion still suffer from some inherent architectural flaws. Moreover, the training-data-scarce scene has not been effectively explored. To this end, this paper proposes an innovative multiscale recurrent fusion transformer (MRFT) framework, which contains three key components, i.e. multiscale transformer feature encoder, recurrent feature fusion module, and prior knowledge injection. Specifically, the multiscale transformer feature encoder consists of a prior-injected context encoder (PICE) and a multiscale local feature encoder (MLFE). PICE leverages the vanilla self-attention mechanism to extract the global context correlating spatially-distant contents for distinguishing long-distance false contours. MLFE exploits the local self-attention mechanism with varied window sizes to capture different-scale detail features. Then, a hierarchical recurrent decoder (HRD) is proposed as the recurrent feature fusion module to fuse multiscale visual information with global guidance. Via the circular query-key mechanism, global-to-local information is progressively fused. Furthermore, we propose a two-stage alternating optimization strategy for prior knowledge injection. By pre-parameterizing the global auxiliary priors, the training dilemma on the data-scarce domain is significantly alleviated. Extensive analyses on multiple benchmark datasets demonstrate the superiority of our MRFT in terms of quantitative measures and aesthetic effects.

Fast Blind Decontouring Network

Contouring artifacts usually appear in large and smooth flat areas, which are caused by many widely used processes such as bit-depth expansion, compression, image sharpening and contrast enhancement. Unfortunately, recent decontouring methods were mainly designed for specific and non-blind degradations, which significantly reduces the generalization ability of these methods when applied to complex and various real-world false contours. Therefore, this paper explores the blind decontouring problem by proposing a blind decontouring network (BDCN). Instead of directly training a decontouring network with mixed degradations, the proposed model consists of two independent modules, i.e., a flat region detection module (FDM) and a decontouring module (DCM). The FDM is designed to extract flat region masks robust to various false contours, which can preserve texture details from global smoothing. Then, the task of DCM becomes simply smoothing different contouring artifacts. Both the FDM and DCM are designed with a lightweight architecture and reparameterization strategy. Experimental results on both synthetic and real-world contouring artifacts demonstrate the effectiveness and generalization of the proposed method.

Frequency domain-based bit-depth enhancement method and dealing with different bit-depth degradation

In this paper, we present the effectiveness of a frequency domain-based loss function using the discrete cosine transform (DCT) for the bit-depth enhancement (BDE) problem that recovers high-bit-depth images from low-bit-depth images. By minimizing the loss between the DCT coefficients, it is expected to recover smooth luminance changes by suppressing extra frequency components caused by the weak gradients contained in the false contour artifacts. Moreover, we proposed a frequency domain-based multi-level BDE method to deal with different bit-depth degradation. The proposed multi-level BDE method identifies the bit-depth of the input image by embedding the bit-depth information in the frequency domain, and it recovered the missing lower bits appropriately. Experimental results show that the model optimized with frequency-based loss outperforms the model optimized with other losses in the comparison considering the objective and subjective results. Furthermore, we show that the proposed multi-level BDE method is effective for more severe bit-depth degradation on a specific benchmark dataset.

Foreground segmentation and location of coal and gangue under complex similar background

To improve the foreground segmentation and location accuracy of complex coal gangue images with gray histogram distribution close to the unimodal shape, a contour detection algorithm of the grayscale fluctuation matrix is proposed. The contour and non-contour pixels of coal and gangue images are investigated, and the result indicates that the gray values of the pixels around the contour exhibit the non-uniform distribution, and the gray value changes in different directions are significantly different. Accordingly, a grayscale fluctuation matrix is built by calculating the change amplitude of pixels in different directions, and multiple features are extracted from the grayscale fluctuation matrix to realize the target contour segmentation. Furthermore, the contour is optimized using the historical and future information of the contour image, thus effectively removing numerous false contours, reproducing some hidden contours and increasing segmentation accuracy. This method has high accuracy, and the maximum error rates of the pixel area and center coordinate of contour detection are 4.404% and 3.18% respectively. This study provides a feasible solution to the edge detection and segmentation of images with similar and complex backgrounds.

A Breast Cancer Contour Detection With Level Sets and SVM Model

Level sets have been widely used to isolate features of breast tumors in ultrasound images. However, region-based methods always produce multiple contours. Since tumors are regularly undefined from the shadows and muscular regions in breast ultrasound images, computerized tumors location and arrangement is significantly difficult. Therefore, the authors introduce a breast cancer contour detection model using support vector machine (SVM) as a binary classification. Features of the binary SVM model were extracted from level sets and FM method (the fusion of ultrasound, elasticity, and Doppler images). The model was accurately able to predict a correct breast tumor contour from false contours which were segmented by region-based level sets. The proposed method was evaluated on 60 datasets collected by professional radiologists at the Thammasat University Hospital of Thailand. From the experimental results, the breast cancer contours were detected correctly with high accuracy. The percentage of correct detection was 93%.

Residual-Guided Multiscale Fusion Network for Bit-Depth Enhancement

Bit-depth enhancement (BDE) is a challenging task due to stubborn false contour artifacts and disappeared detailed information. Given the mixture of structural distortions and real edges in low bit-depth (LBD) images, both large and small receptive fields (RFs) are critical for BDE tasks. However, even powerful state-of-the-art CNN-based methods can hardly capture sufficient LBD features under multiple RFs. This paper proposes a residual-guided multiscale fusion network (RMFNet) to explore multiscale features in a residual manner. We find that the shuffling operation provides desired multiscale inputs for effectively distinguishing false contours from real edges without any loss of information. Therefore, we shuffle LBD images to multiple scales and then fully extract residual features under different RFs with corresponding subnets. To facilitate interscale guidance from the global context to the local context, we progressively transfer the encoded residual features between adjacent subnets from top to bottom. We further propose a dual-branch depthwise group fusion (DDGF) module to fully capture inter- and inner correlations of multiscale features with fewer parameters. Finally, extensive experiments show that our algorithm achieves excellent performance improvement both quantitatively and qualitatively, verifying its effectiveness.

Building Classification Algorithm Based on Improved Hough Transform and Geographic Information System

Although determining the type of building can be useful for 3D reconstruction, image retrieval, and other applications, it is often difficult to do so automatically. A building classification algorithm using a geographic information system (GIS) is proposed in this paper. The typical adjustment of building polygon, local convex-concave simplification of large-area polygon contour, and local exaggeration of competing polygon groups merging in small-area polygon is the main topics of this paper. The preliminary outline area is obtained by analyzing the corner characteristics of two basic types of buildings in urban buildings using a large-scale morphological screen. The classification statistics of all kinds of corners are carried out using the improved Hough transform and the proposed line segment and corner optimization algorithm, and the automatic classification of flat-roofed and nonflat-roofed buildings photographed by digital cameras is realized. The experimental results show that the algorithm simplifies the polygons in a reasonable way while maintaining the block shape. Furthermore, the optimization algorithm proposed in this paper effectively eliminates the influence of false contours, allowing for high-accuracy building type judgment.

TANet: Target Attention Network for Video Bit-Depth Enhancement

Video bit-depth enhancement (VBDE) reconstructs high-bit-depth (HBD) frames from a low-bit-depth (LBD) video sequence. As neighboring frames contain a considerable amount of complementary information related to the center frame, it is vital for VBDE to exploit neighboring frames as much as possible. Conventional VBDE algorithms with explicit alignment across frames attempt to warp each neighboring frame to the center frame with estimated optical flow, taking into account only pairwise correlation. Most spatiotemporal fusion approaches involve direct concatenation or 3D convolution and treat all features equally, failing to focus on information related to the center frame. Therefore, in this paper, we introduce an improved nonlocal block as a global attentive alignment (GAA) module, which takes the whole input video sequence into consideration to capture features that are globally correlated, to perform implicit alignment. Furthermore, given the bulk of features extracted from the center and neighboring frames, we propose target-guided attention (TGA). TGA can exploit more center-frame-related details and facilitate feature fusion. The proposed network (dubbed TANet) is capable of effectively eliminating false contours and recovering the center frame in high quality, as demonstrated by the experimental results. TANet outperforms state-of-the-art models in terms of both PSNR and SSIM with low time consumption.

Dynamic false contour evaluation method based on just noticeable distortion model for microdisplays

The dynamic false contour phenomenon of digital driving microdisplays affects the display quality seriously when displaying moving images. By analyzing the generation mechanism of dynamic false contour and considering the visual characteristics of the human eyes, this paper proposes a just noticeable distortion integral method to quantify the dynamic false contour level of different scan strategies. A series of experiments are designed to verify the reliability of the proposed evaluation method. Combining the linear pulse width modulation scan method and the subframe scan method, a centralized subframe scan method is proposed to alleviate the dynamic false contour phenomenon. The system verification platform is performed on a silicon-based OLED microdisplay with a resolution of 1280×1024. When the parameter K of centralized subframe scan method is not less than 4, the average Peak-Signal-to-Noise Ratio of image data set is above 35db, and the display effect is close to the original image.

Edge Detection of Agricultural Products Based on Morphologically Improved Canny Algorithm

The traditional canny edge detection algorithm has its limitations in the aspect of antinoise interference, and it is susceptible to factors such as light. To solve these defects, the Canny algorithm based on morphological improvement was proposed and applied to the detection of agricultural products. First, the algorithm uses the open and close operation of morphology to form a morphological filter instead of the Gaussian filter, which can remove the image noise and strengthen the protection of image edge. Second, the traditional Canny operator is improved to increase the horizontal and vertical templates to 45° and 135° to improve the edge positioning of the image. Finally, the adaptive threshold segmentation method is used for rough segmentation, and on this basis, double detection thresholds are used for further segmentation to obtain the final edge points. The experimental results show that compared with the traditional algorithm applied to the edge detection of agricultural products, this algorithm can effectively avoid the false contour caused by illumination and other factors and effectively improve the antinoise interference while more accurate and fine detection of the edge of real agricultural products.

BE-ACGAN: Photo-realistic residual bit-depth enhancement by advanced conditional GAN

Since more demands for high quality visualization have been raised in various fields, monitors with higher bit-depth (HBD) become popular in recent years. However, most digital images are at low bit-depth (LBD) and usually of low visual quality with annoying false contours when displayed on HBD monitors directly. To reconstruct visually pleasant HBD images, many bit-depth enhancement (BE) algorithms have been proposed from various aspects, but the recovered HBD images are usually unsatisfactory with conspicuous false contours or over-blurred textures. Inspired by discriminative learning, we propose a residual BE algorithm based on advanced conditional generative adversarial network (BE-ACGAN), in which the discriminator adversarially helps assess image quality and train the generator to achieve more photo-realistic recovery performance. Besides, since it is hard to distinguish between the reconstructed and real HBD images with similar structures, the discriminator takes residual images as input and further takes LBD images as conditions to achieve more reliable performance. In addition, we present a novel loss function to deal with the difficulty of unstable adversarial training. The proposed algorithm outperforms the state-of-the-art methods on large-scale benchmark datasets. Source codes are available at https://github.com/TJUMMG/BE- ACGAN/.

CNN-Based Suppression of False Contour and Color Distortion in Bit-Depth Enhancement.

It is a challenge to transmit and store the massive visual data generated in the Visual Internet of Things (VIoT), so the compression of the visual data is of great significance to VIoT. Compressing bit-depth of images is very cost-effective to reduce the large volume of visual data. However, compressing the bit-depth will introduce false contour, and color distortion would occur in the reconstructed image. False contour and color distortion suppression become critical issues of the bit-depth enhancement in VIoT. To solve these problems, a Bit-depth Enhancement method with AUTO-encoder-like structure (BE-AUTO) is proposed in this paper. Based on the convolution-combined-with-deconvolution codec and global skip of BE-AUTO, this method can effectively suppress false contour and color distortion, thus achieving the state-of-the-art objective metric and visual quality in the reconstructed images, making it more suitable for bit-depth enhancement in VIoT.

Dynamic False Contour Quantification and Improvement Strategy of Digital Driven OLED

Dynamic False Contour Quantification and Improvement Strategy of Digital Driven OLED

An Occlusion‐aware Edge‐Based Method for Monocular 3D Object Tracking using Edge Confidence

AbstractWe propose an edge‐based method for 6DOF pose tracking of rigid objects using a monocular RGB camera. One of the critical problem for edge‐based methods is to search the object contour points in the image corresponding to the known 3D model points. However, previous methods often produce false object contour points in case of cluttered backgrounds and partial occlusions. In this paper, we propose a novel edge‐based 3D objects tracking method to tackle this problem. To search the object contour points, foreground and background clutter points are first filtered out using edge color cue, then object contour points are searched by maximizing their edge confidence which combines edge color and distance cues. Furthermore, the edge confidence is integrated into the edge‐based energy function to reduce the influence of false contour points caused by cluttered backgrounds and partial occlusions. We also extend our method to multi‐object tracking which can handle mutual occlusions. We compare our method with the recent state‐of‐art methods on challenging public datasets. Experiments demonstrate that our method improves robustness and accuracy against cluttered backgrounds and partial occlusions.

Recursively Partitioned Clipped Histogram Equalization Techniques for Preserving Image Features

In this work, we present recursively partitioned clipped histogram equalization (RPCHE) techniques, viz., recursive median partitioned clipped histogram equalization (RMDPCHE) and recursive mean partitioned clipped histogram equalization techniques for better quality images. They adopt the feature of histogram equalization (HE) in terms of simplicity, the feature of recursive histogram partition and histogram equalization to maintain low absolute mean brightness error and the feature of clipped histogram equalization in terms of control on over enhancement. In addition to these, the RPCHE methods are devoid of intensity compression, resulting in no gray level loss, and retain the total number of gray, assure uniform degree enhancement of gray to get over all image enhancement and assure no false contouring of objects. In RMDPCHE, the histogram is divided recursively with median gray level, and later these sub histograms are restricted to individual clipped threshold and finally conventional HE is applied on these clipped histograms to get over all equalized image. Experimental results show the superiority of the proposed methods over the state-of-art HE methods in terms of preserving image features with uniform degree of enhancement. They are able to achieve maximum entropy with minimum gradient magnitude similarity deviation. These ensure the objects in the processed image to have fine contours with natural enhancement.

Halftoning-based BTC image reconstruction using patch processing with border constraint

This paper presents a new halftoning-based block truncation coding (HBTC) image reconstruction using sparse representation framework. The HBTC is a simple yet powerful image compression technique, which can effectively remove the typical blocking effect and false contour. Two types of HBTC methods are discussed in this paper, i.e., ordered dither block truncation coding (ODBTC) and error diffusion block truncation coding (EDBTC). The proposed sparsity-based method suppresses the impulsive noise on ODBTC and EDBTC decoded image with a coupled dictionary containing the HBTC image component and the clean image component dictionaries. Herein, a sparse coefficient is estimated from the HBTC decoded image by means of the HBTC image dictionary. The reconstructed image is subsequently built and aligned from the clean, i.e. non-compressed image dictionary and predicted sparse coefficient. To further reduce the blocking effect, the image patch is firstly identified as “border” and “non-border” type before applying the sparse representation framework. Adding the Laplacian prior knowledge on HBTC decoded image, it yields better reconstructed image quality. The experimental results demonstrate the effectiveness of the proposed HBTC image reconstruction. The proposed method also outperforms the former schemes in terms of reconstructed image quality.

Adaptive Debanding Filter

Banding artifacts, which manifest as staircase-like color bands on pictures or video frames, is a common distortion caused by compression of low-textured smooth regions. These false contours can be very noticeable even on high-quality videos, especially when displayed on high-definition screens. Yet, relatively little attention has been applied to this problem. Here we consider banding artifact removal as a visual enhancement problem, and accordingly, we solve it by applying a form of content-adaptive smoothing filtering followed by dithered quantization, as a post-processing module. The proposed debanding filter is able to adaptively smooth banded regions while preserving image edges and details, yielding perceptually enhanced gradient rendering with limited bit-depths. Experimental results show that our proposed debanding filter outperforms state-of-the-art false contour removing algorithms both visually and quantitatively.

Inhomogeneous Image Segmentation Using Hybrid Active Contours Model With Application to Breast Tumor Detection

The most fatal and frequent cancer amongst women is breast cancer. Mammography provides timely detection of lumps and masses in breast tissue, but effective diagnosis requires accurately identifying malignant tumor boundaries, which remains challenging, particularly for images with inhomogeneous regions. Therefore, we propose an active contour method based on a reformed combined local and global fitted function to address breast tumor segmentation. This combined function is strengthened by a proposed average energy driving function to capture obscure boundaries for regions of interest more precisely from inhomogeneous images. Including a p-Laplace term eliminates reinitialization requirements and suppresses false contours in the segmentation. Bias field signal, which causes image homogeneity corruption, is estimated by bias field initialization to ensure independence from the initial contour position. Local and global fitted models are incorporated by introducing bias fields within them. The proposed method was tested on the MIAS MiniMammographic Database, with quantitative analysis to calculate its accuracy, effectiveness, and efficiency. Experimentation confirmed the proposed method provided superior results compared with previous state-of-the-art methods.

Spatiotemporal Symmetric Convolutional Neural Network for Video Bit-Depth Enhancement

In contrast to the high sensitivity of human eyes and rapid development of modern display devices in terms of dynamic range, mainstream multimedia sources are generally at relatively lower bit depths (BDs). Therefore, BD enhancement (BDE), which attempts to transform low-BD multimedia sources into high-BD sources, is considered of significant research value. Current BDE algorithms are based on images rather than videos. However, for massive numbers of videos, temporal continuity among frames should be considered. Thus, in this paper, we propose a spatiotemporal symmetric BDE network for videos based on an encoder–decoder network. Consecutive frames are input into five subnets in the encoder, where the convolutional filters in the temporal symmetric subnets share the same weights to achieve lower model complexity. In addition, symmetric skip connections are introduced between the symmetric convolutional/deconvolutional layers of the encoder/decoder to pass features and alleviate the gradient diffusion problem. The experimental results show that our model can efficiently eliminate false contours and chroma distortions. The model significantly outperforms state-of-the-art image BDE algorithms and single-frame baseline models in terms of PSNR and SSIM.