Based on the study of Fast Block Matching Algorithms (FBMA) for motion estimation, this paper attempts to make an overview of fast block matching algorithms First, it describes the theory of motion estimation Then it reveals the key technique for improving the algorithm efficiency, analyses the correlative algorithms and compares their properties At the end, the future trend of motion estimation research is discussed

Block Matching Algorithms for Motion Estimation

Image denoising has been a well-studied problem for imaging systems, especially imaging sensors. Despite remarkable progress in the quality of denoising algorithms, persistent challenges remain for a wide class of general images. In this paper, we present a new concept of sequence-to-sequence similarity (SSS). This similarity measure is an efficient method to evaluate the content similarity for images, especially for edge information. The approach differs from the traditional image processing techniques, which rely on pixel and block similarity. Based on this new concept, we introduce a new SSS-based filter for image denoising. The new SSS-based filter utilizes the edge information in the corrupted image to address image denoising problems. We demonstrate the filter by incorporating it into a new SSS-based image denoising algorithm to remove Gaussian noise. Experiments are performed over synthetic and experimental data. The performance of our methodology is experimentally verified on a variety of images and Gaussian noise levels. The results demonstrate that the proposed method’s performance exceeds several current state-of-the-art works, which are evaluated both visually and quantitatively. The presented framework opens up new perspectives in the use of SSS methodologies for image processing applications to replace the traditional pixel-to-pixel similarity or block-to-block similarity.

Sequence-to-Sequence Similarity-Based Filter for Image Denoising

In practice, acquired color images are inevitably noisy, and filtering/denoising procedure is used to suppress the noise. Although numerous denoising techniques have been proposed, they are not universally efficient in all considered practical situations. There are also contradictory requirements to color image denoising and their priority can be different and strongly dependent on the situation at hand. This also complicates the choice of a proper filter. Color images can be filtered in a component-wise (e.g., R, G, and B components separately) and in 3D (vector) manner. The latter group of approaches usually produces better results but has certain shortcomings and is less developed. One more aspect is that filtering efficiency is often analyzed and compared using only standard metrics (criteria) often ignoring recently designed visual quality metrics. Finally, before starting applying image denoising, it is good to understand how efficient can it be and is it worth to perform such afiltering. Then, the task of predicting denoising efficiency becomes very interesting.

DCT-Based Color Image Denoising: Efficiency Analysis and Prediction

We proposed an optimum de-noising filter using wavelet threshold for the removal of adaptive white Gaussian noise from degraded medical images/colour video sequence based on system noise calculatio...

Fast and Efficient Filter Using Wavelet Threshold for Removal of Gaussian Noise from MRI/CT Scanned Medical Images/Color Video Sequence

A novel method for filtering images contaminated by mixed (additive-multiplicative) noise is substantiated and implemented for the first time. The method includes several stages: the formation of similar structures in 3D space, homomorphic transformation, a 3D filtering approach based on a sparse representation in the discrete cosine transform space, inverse homomorphic transformation, and final post-processing that involves bilateral filtering and the reconstruction of edges and details. A physical interpretation of the filtering procedure under mixed noise conditions is given, and a filtering block diagram is developed. Numerous experiments based on the developed method have confirmed its superiority in term of conventional criteria, such as the structural similarity index measure and the peak signal-to-noise ratio, as well as in term of visual image quality via human perception.

3D Filtering of Images Corrupted by Additive-Multiplicative Noise

Fuzzy color video filtering technique for sequences corrupted by additive Gaussian noise

A novel method for the denoising of color videos corrupted by additive noise is presented in this paper. The proposed technique consists of three principal filtering steps: spatial, spatiotemporal, and spatial postprocessing. In contrast to other state-of-the-art algorithms, during the first spatial step, the eight gradient values in different directions for pixels located in the vicinity of a central pixel as well as the R, G, and B channel correlation between the analogous pixels in different color bands are taken into account. These gradient values give the information about the level of contamination then the designed fuzzy rules are used to preserve the image features (textures, edges, sharpness, chromatic properties, etc.). In the second step, two neighboring video frames are processed together. Possible local motions between neighboring frames are estimated using block matching procedure in eight directions to perform interframe filtering. In the final step, the edges and smoothed regions in a current frame are distinguished for final postprocessing filtering. Numerous simulation results confirm that this novel 3D fuzzy method performs better than other state-of-the-art techniques in terms of objective criteria (PSNR, MAE, NCD, and SSIM) as well as subjective perception via the human vision system in the different color videos.

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Fuzzy filtering method for color videos corrupted by additive noise.

A filtering method for color videos contaminated by additive noise is presented. The proposed framework employs three filtering stages: spatial similarity filtering, neighboring frame denoising, and spatial post-processing smoothing. The difference with other state-of- the-art filtering methods, is that this approach, based on fuzzy logic, analyses basic and related gradient values between neighboring pixels into a 7 fi 7 sliding window in the vicinity of a central pixel in each of the RGB channels. Following, the similarity measures between the analogous pixels in the color bands are taken into account during the denoising. Next, two neighboring video frames are analyzed together estimating local motions between the frames using block matching procedure. In the final stage, the edges and smoothed areas are processed differently in a current frame during the post-processing filtering. Numerous simulations results confirm that this 3D fuzzy filter perform better than other state-of-the- art methods, such as: 3D-LLMMSE, WMVCE, RFMDAF, FDARTF G, VBM3D and NLM, in terms of objective criteria (PSNR, MAE, NCD and SSIM) as well as subjective perception via human vision system in the different color videos. An efficiency analysis of the designed and other mentioned filters have been performed on the DSPs TMS320 DM642 and TMS320DM648 by Texas Instruments through MATLAB and Simulink module showing that the novel 3D fuzzy filter can be used in real-time processing applications.

Hector Montenegro-Monroy

Papers

Fuzzy color video filtering technique for sequences corrupted by additive Gaussian noise

Fuzzy filtering method for color videos corrupted by additive noise.

3D filtering technique in presence of additive noise in color videos implemented on DSP