Fine Image Details Research Articles

Visual Arts Enhance Instruction in Observation and Analysis of Microscopic Forms in Developmental and Cell Biology

Two important skills for scientists in developmental and cell biology, as well as in fields such as neurobiology, histology and pathology, are: 1) observation of features and details in microscopic images of cells, and 2) quantification of cellular features observed in microscopic images. However, current training in developmental and cell biology does not emphasize observation and quantitative analysis of microscopic images, and it is unclear how best to teach students these skills. Here, we describe our experiences applying visual artistic approaches to instruct undergraduate and graduate students in how to observe and analyze cellular forms in microscopic images. At Loyola Marymount University, we used representational drawing to enhance undergraduate students’ skills in observation of fine cellular details in microscopic images of embryos. At Touro University California, we paired abstract paintings with microscopic images of tissues to engage masters and medical students in learning quantitative measurements of cellular features. Overall, this paper explains specific ways in which visual arts can be used to instruct and engage students in observation and analysis of microscopic images of cells and tissues.

Artificial Bee Colony Optimization Based Despeckling Framework for Ultrasound Images

This paper proposed an artificial bee colony optimization (ABC) algorithm based despeckling framework to overcome the effect of speckle noise present in real ultrasound images. A low pass filter and fast non-local mean filter along with Artificial Bee Colony (ABC) optimization algorithm are used for the quality enhancement of ultrasound images. The output results obtained for the real ultrasound images filtered with the proposed approach and the other most studied approaches discussed in the literature. The outperformance of the proposed method is verified by calculation of peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), and structure similarity index (SSIM) quality measures. The proposed filtering approach is tested on eight real clinical ultrasound images of adrenal gland, appendicitis, bladder, pancreas, parathyroid gland, scrotal gland, thoracic wall, and uterus. The experimental results yield that the quantitative and qualitative results of the proposed framework are better than benchmark despeckling methods compared to real ultrasound images. Further, the proposed framework also preserves the fine details in real ultrasound images.

Simultaneous Intensity Bias Estimation and Stripe Noise Removal in Infrared Images Using the Global and Local Sparsity Constraints

Infrared (IR) images are often contaminated by obvious intensity bias and stripes, which severely affect the visual quality and subsequent applications. It is challenging to eliminate simultaneously the mixed nonuniformity noise without blurring the fine-image details in low-textured IR images. In this article, we present a new model for simultaneous intensity bias correction and destriping through introducing two sparsity constraints. One is that model fit on the intensity bias should be as accurate as possible. A bivariate polynomial model is built to characterize the global smoothness of the intensity bias. The other constraint is that the unidirectional variational sparse model can concisely represent the direction characteristic of stripe noise. A computationally efficient numerical algorithm based on split Bregman iteration is used to solve the complex optimization problem. The proposed method is fundamentally different from the existing denoising techniques and simultaneously estimates the sharp image, intensity bias, and stripe components. Significant improvement on image quality is achieved on both simulated and real studies. Both qualitative and quantitative comparisons with the state-of-the-art correction methods demonstrate its superiority.

Projection-to-Projection Translation for Hybrid X-ray and Magnetic Resonance Imaging

Hybrid X-ray and magnetic resonance (MR) imaging promises large potential in interventional medical imaging applications due to the broad variety of contrast of MRI combined with fast imaging of X-ray-based modalities. To fully utilize the potential of the vast amount of existing image enhancement techniques, the corresponding information from both modalities must be present in the same domain. For image-guided interventional procedures, X-ray fluoroscopy has proven to be the modality of choice. Synthesizing one modality from another in this case is an ill-posed problem due to ambiguous signal and overlapping structures in projective geometry. To take on these challenges, we present a learning-based solution to MR to X-ray projection-to-projection translation. We propose an image generator network that focuses on high representation capacity in higher resolution layers to allow for accurate synthesis of fine details in the projection images. Additionally, a weighting scheme in the loss computation that favors high-frequency structures is proposed to focus on the important details and contours in projection imaging. The proposed extensions prove valuable in generating X-ray projection images with natural appearance. Our approach achieves a deviation from the ground truth of only 6% and structural similarity measure of 0.913 ± 0.005. In particular the high frequency weighting assists in generating projection images with sharp appearance and reduces erroneously synthesized fine details.

Deconvolution of light sheet microscopy recordings

We developed a deconvolution software for light sheet microscopy that uses a theoretical point spread function, which we derived from a model of image formation in a light sheet microscope. We show that this approach provides excellent blur reduction and enhancement of fine image details for image stacks recorded with low magnification objectives of relatively high NA and high field numbers as e.g. 2x NA 0.14 FN 22, or 4x NA 0.28 FN 22. For these objectives, which are widely used in light sheet microscopy, sufficiently resolved point spread functions that are suitable for deconvolution are difficult to measure and the results obtained by common deconvolution software developed for confocal microscopy are usually poor. We demonstrate that the deconvolutions computed using our point spread function model are equivalent to those obtained using a measured point spread function for a 10x objective with NA 0.3 and for a 20x objective with NA 0.45.

Adaptive Texture-Preserving Denoising Method Using Gradient Histogram and Nonlocal Self-Similarity Priors

Natural image priors play an important role in image denoising, and various prior-based methods have been widely proposed for noise removal. However, these methods tend to smooth the fine image textures while suppressing noise, degrading the image visual quality. To address this problem, in this paper, we propose an adaptive texture-preserving denoising method. In contrast to most existing prior-based denoising methods, two types of priors [gradient histogram matching priors and nonlocal self-similarity (NSS) priors] are proposed, and their combination is used for image denoising. We introduce a hyper-Laplacian distribution of the gradient histogram matching prior, which enforces the gradient histogram of the denoised image to be as close as possible to the estimated reference histogram from the original image. Meanwhile, the proposed model obtained by introducing the NSS priors effectively preserves fine image details and generates sharp image edges. To improve the accuracy of the method, a content-adaptive parameter selection scheme based on edge detection filters is proposed. Moreover, the optimization problem with two types of priors and the content-adaptive parameter added into the objective function becomes a challenging non-convex optimization problem. To effectively solve this problem, we have developed a new numerical solution based on augmented Lagrangian multipliers and alternating minimization scheme. The experimental results demonstrate that the proposed method effectively preserves the texture features of the denoised images and outperforms several variational methods and other state-of-the-art methods in terms of various evaluation indices and visual quality, especially at medium and high noise levels.

Detail Preserved Single Image Dehazing Algorithm Based on Airlight Refinement

Single-image haze removal is important for many practical applications (e.g., surveillance). However, dehazed results of existing algorithms tend to be oversmoothed with missing fine image details. This drawback is caused by two factors: inaccurate airlight estimations and disregarding multiple scattering. In this paper, we propose a detail-preserving image dehazing algorithm based on two key priors, namely, the depth-edge aware prior and the airlight impact regularity prior. The proposed algorithm makes contributions in both the haze removal step and the postprocessing step. First, based on the depth-edge aware prior, an airlight refinement algorithm is proposed. The gradient strength of the minimum channel is employed to calculate punishment weights to smooth the dark channel. Second, based on the airlight impact regularity prior, an adaptive sharpening model that considers the refined airlight to determine the sharpening strength value is established to enhance levels of detail. Experimental results demonstrate that the proposed algorithm cannot only effectively remove haze but can also enhance levels of detail to thus outperform the state of the art on a wide variety of images.

A Perceptually Inspired New Blind Image Denoising Method Using $L_{1}$ and Perceptual Loss

Recently, discriminative learning-based denoising methods have received much attention and have been studied to a large extent because of their high denoising performance with significantly shorter inference time compared to model based denoising methods. In this paper, we consider a perceptually motivated blind image denoising problem, which removes various levels of noise from an observed noisy image with a single model and produces visually pleasant images. It is well known that very few blind image denoising methods are available and they have shown the limited quality of restored images because they sacrifice the fine image details during the denoising process due to over-smoothing, resulting in visually unpleasant images. To overcome these problems, we propose a novel loss function that encourages the network to restore noise free images by focusing on the perceived visual quality. Then, the proposed loss function is adopted to an encoder-decoder network with skip connections that produces visually pleasant images with highly preserved fine image details. Further, this method is robust and constantly performs well for several unknown noise levels. Our extensive experimental results show the superiority of the proposed method both quantitatively and qualitatively.

A Unified Method for Digital Super-Resolution and Restoration in Infrared Microscopy Imaging

Infrared (IR) imaging systems are known to have a range of sensor and optical limitations that result in degraded imagery. Fixed pattern noise (FPN), resulting from pixel-to-pixel response nonuniformity, is a dominant source of error that manifests in collected imagery through the appearance of temporally and spatially correlated noise patterns that are mixed with each image. Furthermore, finite detector size coupled with imperfect system optics can introduce blurring effects and aliasing, ultimately reducing resolution in acquired images. Here, we propose a unified method to reduce FPN and recover high-frequency image content in IR microscopy images. The proposed method uses regularized nonlocal means to highlight spatial features in the scene while maintaining fine textural image details. We derive an iterative optimization method based upon a gradient descent minimization strategy that applies a Wiener deconvolution in each iteration to estimate the blur artifacts. The method is implemented within an embedded mid-wave IR imaging system for microscopy applications. We demonstrate a reduction in FPN and blurring artifacts, achieving improved image resolution in the reconstructed images that are apparent in recovered details on scene objects.

The Effects of High Dynamic Range on Fingerprint Images Processed by Photoshop.

High dynamic range (HDR) imaging is a function that combines five images with different exposures into a single image. This technique may provide fine ridge details of fingerprint images for forensic latent fingerprint examination. Therefore, viewing fingerprints under optimal conditions is of paramount importance. This paper analyzes HDR and non-HDR photos by using the Michelson contrast formula. The Michelson formula will provide a measurement to determine whether better contrast between the background and print can be achieved using the HDR function and if the background color affects the quality of the images. Two hypothesis were tested: (i) the HDR image provides more details of fingerprints with a better tone, greater clarity, and contrast than a normally exposed image regardless of the background color; (ii) the background color does not affect the quality of HDR fingerprint images overall, but the multi-color background may increase the contrast of HDR fingerprint images in some cases.

Spatially adaptive total generalized variation-regularized image deblurring with impulse noise

Image deblurring with impulse noise is a typical ill-conditioned problem that requires regularization techniques to guarantee stable and high-quality imaging. According to the statistical properties of impulse noise, an L1-norm data fidelity term and a total variation (TV) regularizer have been combined to contribute a popular regularization model. However, traditional TV-regularized variational models usually suffer from staircase-like artifacts in homogenous regions resulting in visual quality degradation. To eliminate undesirable artifacts, we propose a high-order variational model by replacing the TV with a detail-preserving total generalized variation (TGV) regularizer. To further enhance imaging performance, the spatially adaptive regularization parameters are automatically selected, based on local image features to promote the high-order TGV-regularized variational model. The resulting nonsmooth optimization problem is effectively handled using the alternating direction method of multipliers-based numerical method. The proposed variational model has the capacity to remove blurring and impulse noise effects while maintaining fine image details. Comprehensive experiments were conducted on both gray and color images to compare our proposed method with several state-of-the-art image restoration methods. Experimental results have demonstrated its superior performance in terms of quantitative and qualitative image quality evaluations.

Non-subsampled shearlet transform-based image fusion using modified weighted saliency and local difference

Existing image fusion methods can not efficiently capture significant edges, texture and fine details of the source images due to inefficient fusion framework. In addition, for objective evaluation of fusion algorithms, not much attention is given to simultaneously measure both texture and structural information of the source images which are preserved in the fused image. To address these issues, non-subsampled shearlet transform (NSST) is used to decompose pre-registered source images into low- and high-frequency components. These low- and high-frequency coefficients are fused by using our proposed modified weighted salience and local difference fusion rules, respectively. To enrich edge information in the fused image, Canny edge detector with scale multiplication is employed. Moreover, a metric QTS is proposed to jointly measure both texture and structural information present in the fused image. The proposed metric is formulated on the basis of local standard deviation filtering, local information entropy, and local difference filtering. Both subjective and objective results validate the proposed fusion framework and the metric QTS.

Denoising images of dual energy X-ray absorptiometry using non-local means filters.

In general, the image quality of high and low energy images of dual energy X-ray absorptiometry (DXA) suffers from noise due to the use of a small amount of X-rays. Denoising of DXA images could be a key process to improve a bone mineral density map, which is derived from a pair of high and low energy images. This could further improve the accuracy of diagnosis of bone fractures and osteoporosis. This study aims to develop and test a new technology to improve the quality, remove the noise, and preserve the edges and fine details of real DXA images. In this study, a denoising technique for high and low energy DXA images using a non-local mean filter (NLM) was presented. The source and detector noises of a DXA system were modeled for both high and low DXA images. Then, the optimized parameters of the NLM filter were derived utilizing the experimental data from CIRS-BFP phantoms. After that, the optimized NLM was tested and verified using the DXA images of the phantoms and real human spine and femur. Quantitative evaluation of the results showed average 24.22% and 34.43% improvement of the signal-to-noise ratio for real high and low spine images, respectively, while the improvements were about 15.26% and 13.55% for the high and low images of the femur. The qualitative visual observations of both phantom and real structures also showed significantly improved quality and reduced noise while preserving the edges in both high and low energy images. Our results demonstrate that the proposed NLM outperforms the conventional method using an anisotropic diffusion filter (ADF) and median techniques for all phantom and real human DXA images. Our work suggests that denoising via NLM could be a key preprocessing method for clinical DXA imaging.

Fourier Based Discrete Shearlet Transform for Speckle Noise Reduction in Medical Ultrasound Images

Background: Medical ultrasonic images are usually degraded by a special kind of noise called ’speckle'. The speckle noises usually have an effect more on edges and fine details of an ultrasound images which lead to reduction in their contrast resolution consequently create difficulties in the diagnosis of illnesses. Methods: In this paper, to reduce the speckle noise of medical ultrasound image, we have proposes a denoising approach by using ’discrete shearlet transform' based on pseudo-polar fast Fourier transform. The mathematical structure of present method is briefly discussed. To illustrate the efficiency of proposed approach, the experimental results of synthesis of some real Ultrasound images are considered. Results: Experimental results illustrate that proposed approach can obtain better performance in terms of Peak Signal To Noise Ratio (PSNR) for ultrasound image denoising. Our numerical tests include a series of computer-simulated and in MATLAB experiments. Keywords: Discrete shearlet transform, fourier transform, image denoising, PSNR, threshold, ultrasound Image.

Improved Despeckle Filtering Technique for Liver Cirrhosis US Images

The liver ultrasonic (US) images suffer from inherent speckle noise, degrading the image quality; thereby, affecting human interpretation and also reducing the accuracy in computer-assisted diagnostic techniques. In this paper, we propose an improved despeckle filtering technique by combining, maximum homogeneity over a pixel neighborhood filtering (MHON) and speckle-reducing anisotropic diffusion filtering (SRAD) using a binary classifier map (BCM). The textural and fine details of residual image are denoised by SRAD filtering. The SRAD filter denoised image and MHON filter denoised image combines to attain BCM. A BCM is generated by examining the local coefficient of dispersion (CoD) assessed using a kernel with the one obtained from a specific region. The proposed method is evaluated on clinical US image set of liver, by assessing the quality factors PSNR, SNR, ISC, LMSE and EPI. Results are compared with specified algorithms separately while accomplishing for proposed method.

Sharpness Improvement for Medical Images Using a New Nimble Filter

It is known that various medical devices introduce blur to recorded images due to many factual yet inevitable limitations. Thus, it is highly desirable to provide an efficient solution for such prevalent problem. In this article, a new sharpening filter is proposed to improve the edges and fine details of medical images. In the proposed filter, the sharpening process is achieved by deducting specific spatial information which is determined expeditiously from the original image itself with the existence of a special tuning weight that controls the amount of the produced sharpness. The aforesaid procedure helped to increase the acutance of edges and improve the overall sharpness significantly in the processed images. The proposed filter was evaluated using two advanced image quality assessment metrics and was compared with four well-known image sharpening techniques. Intensive experiments and comparisons using different 2D and 3D real-degraded medical images revealed that the proposed filter has better performance and properties than several existing techniques, and is more suitable for sharpening medical images.

Salt-and-pepper noise removal using modified mean filter and total variation minimization

The search for effective noise removal algorithms is still a real challenge in the field of image processing. An efficient image denoising method is proposed for images that are corrupted by salt-and-pepper noise. Salt-and-pepper noise takes either the minimum or maximum intensity, so the proposed method restores the image by processing the pixels whose values are either 0 or 255 (assuming an 8-bit/pixel image). For low levels of noise corruption (less than or equal to 50% noise density), the method employs the modified mean filter (MMF), while for heavy noise corruption, noisy pixels values are replaced by the weighted average of the MMF and the total variation of corrupted pixels, which is minimized using convex optimization. Two fuzzy systems are used to determine the weights for taking average. To evaluate the performance of the algorithm, several test images with different noise levels are restored, and the results are quantitatively measured by peak signal-to-noise ratio and mean absolute error. The results show that the proposed scheme gives considerable noise suppression up to a noise density of 90%, while almost completely maintaining edges and fine details of the original image.

Fuzzy Connectives for Efficient Image Reduction and Speeding Up Image Analysis

We discuss non-monotone fuzzy connectives in large-scale image processing. We present an image reduction algorithm capable of differentiating between fine image details and noise in the image, particularly salt and pepper noise. The reduction algorithm is based on mode-like averaging functions. We compare the performance of the proposed method to the alternative reduction methods on artificial images and on two case studies: content-based image retrieval and pedestrian detection. Our algorithm improves the speed of the subsequently applied image analysis methods and allows efficient filtering of salt and pepper noise. Applications to on-board image recognition in autonomous robotic devices are envisaged.

Dithering method for reproducing smoothly changing tones and fine details of natural images on woven fabric

This paper introduces novel methods to improve the aesthetic appearance of rendered grayscale digital images on woven fabric by smoothly changing tones and improving the reproducibility of fine details. The methods are based on stepping dithering, a recently developed dithering method for automatically generating jacquard weave patterns for arbitrary given images. The existing stepping dithering method suffers from two problems. The first problem is the visually unappealing repetition of patterns for input images containing low frequency, smooth gradation regions. The second problem is the low reproducibility of small structures with high frequency relative to mask size. This paper proposes new methods for faithfully rendering arbitrary natural images on jacquard fabric by solving the pattern repetition and low reproducibility problems. The new methods combine two approaches. The first problem is addressed by optimizing the distribution of thresholds in dither masks, while the second problem is addressed by adopting a dynamic binarizing process for an appropriate area of the stepping dither mask. The experiments described herein show that the proposed method successfully improves the appearance of the resulting woven fabric.

A multi-scale non-uniformity correction method based on wavelet decomposition and guided filtering for uncooled long wave infrared camera

In uncooled long-wave infrared (LWIR) imaging systems, non-uniformity of the amplifier in readout circuit will generate significant noise in captured infrared images. This type of noise, if not eliminated, may manifest as vertical and horizontal strips in the raw image and human observers are particularly sensitive to these types of image artifacts. In this paper we propose an effective non-uniformity correction (NUC) method to remove strip noise without loss of fine image details. This multi-scale destriping method consists of two consecutive steps. Firstly, wavelet-based image decomposition is applied to separate the original input image into three individual scale levels: large, median and small scales. In each scale level, the extracted vertical image component contains strip noise and vertical-orientated image textures. Secondly, a novel multi-scale 1D guided filter is proposed to further separate strip noise from image textures in each individual scale level. More specifically, in the small scale level, we choose a small filtering window for guided filter to eliminate strip noise. On the contrary, a large filtering window is used to better preserve image details from blurring in large scale level. Our proposed algorithm is systematically evaluated using real-captured infrared images and the quantitative comparison results with the state-of-the-art destriping algorithms demonstrate that our proposed method can better remove the strip noise without blurring image fine details.