Because of the feasible and impressive fallout, the classical Super-Resolution Reconstruction (SRR) is the contemporary algorithm for improving spatial information and reducing noise and SISR (Single-Image Super-Resolution) method, which is form on the classical SRR, is solely developed for improving spatial information. Disastrously, deficiency of the classical SISR method is conceptually computed from three specifications (b, h, k) and the simulating calculation of the optimized specifications for interpolating the better and higher spatial information images with highest PSNR is so burdersome. For figuring out this issue, the Geman&McClure function is proposed to replace with the ordinary SISR function because this function is conceptually computed from only one specification (T), contrary to three specifications similar to classical SISR method hence this analytic article focuses to offer a novel elementary spatial expanding scheme form on SISR method with modifying Geman&McClure function. Therefore, the fallout of a proposed spatial expanding scheme approximately matches to classical SISR method. From these reason, a novel elementary spatial expanding scheme is easily implemented for real works.

This paper presents a performance analysis of 3 popular optical flow algorithms (2D optical flow block-based full search algorithm (BOF), Horn-Schunk algorithm (HS) and Lucas-Kanade algorithm (LK» under the noise conditions. And the confidence based optical flow algorithm for high reliability (CBOF) is applied on these 3 algorithms over different characteristic of standard sequences with several dB of Additive White Gaussian Noise (A WGN). For algorithm of HS and LK, we also applied the kernel model of Barron, Fleet, and Beauchemin (BFB) on these algorithms in our experiment. Especially in HS algorithm, we also investigate the performance on the best average smoothness weight (a) which is prior evaluated by Darnn K. and Vorapoj P .. These experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have different foreground and background movement characteristic in a level of 0.5 sub-pixel displacement. Each standard sequence has 4 sets of sequence included an original (no noise), AWGN 25 dB (low noise), AWGN 20 dB, and A WGN 15 dB (high noise) respectively which concentrated on Peak Signal to Noise Ratio (PSNR) as the performance indicator in our experiment.

Optical flow is a method for classifying the density velocity or motion vector (MV) in a degree of pixel basis for motion classification of image in video sequences. In actual situation, many unpleasant situations usually generate noises over the video sequences. These unpleasant situations corrupt the performance in efficiency of optical flow. In turn to increase the efficiency of the MV, this research work proposes the performance comparison on linear filter and bidirectional confidential technique for spatial domain optical flow algorithms. Our experiment concentrates on the 3 classical spatial based optical flow algorithms (such as spatial correlation-based optical flow (SCOF), Horn-Schunk algorithm (HS) and Lucas-Kanade algorithm (LK)). Different standard video sequences such as AKIYO, CONTAINER, COASTGUARD, and FOREMAN are comprehensively evaluated to demonstrate the effectiveness results. These video sequences have differences in aspect of action and speed in foreground and background. These video sequences are also debased by the Additive White Gaussian Noise (AWGN) at different noise degree (such as AWGN at 25 dB, 20 dB, and 15 dB consequently). Peak Signal to Noise Ratio (PSNR) is utilized as the performance index in our observation.

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Changing in light condition and representative of noise in sequences cause ineffectively in most of motion estimation techniques. This paper proposes a novel robust spatial correlation optical flow algorithm based on the robust motion estimation and effective confidence technique using bidirectional symmetry of forward and backward flow. Experiment results are comprehensively tested on several standard sequences such as AKIYO, COASTGUARD, CONTAINER, and FOREMAN that have different foreground and background movement characteristic. The experiment is tested under the Additive White Gaussian Noise (A WGN) at several noise power levels (such as A WGN at 25 dB (low noise), A WGN at 20 dB, and A WGN at 15 dB (high noise) respectively) in order to demonstrate effectiveness of the proposed algorithm. Peak Signal to Noise Ratio (PSNR) is used as the performance indicator in our experiment.