Image compression using singular value decomposition by extracting red, green, and blue channel colors

Gani, Shamsul Fakhar Abd; Hamzah, Rostam Affendi; Latip, Ramlan; Salam, Sazilah; Noraqillah, Fatin; Herman, Adi Irwan

doi:10.11591/eei.v11i1.2602

Cited by 3 publications

(2 citation statements)

References 19 publications

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“…1963 linear unit (ReLU) layers to a CNN grows linearly [31], [32]. Pooling layer gradually reduces the input dimension, reducing system parameters and processing, and aiding in the prevention of fitting problems.…”

Section: Methodsmentioning

confidence: 99%

Refining disparity maps using deep learning and edge-aware smoothing filter

Abd Gani,

Miskon,

Hamzah

et al. 2024

Bulletin EEI

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Stereo matching algorithm is crucial for applications that rely on three-dimensional (3D) surface reconstruction, producing a disparity map that contains depth information by computing the disparity values between corresponding points from a stereo image pair. In order to yield desirable results, the proposed stereo matching algorithm must possess a high degree of resilience against radiometric variation and edge inconsistencies. In this article convolutional neural network (CNN) is employed in the first stage to generate the raw matching cost, which is subsequently filtered with a bilateral filter (BF) and applied with cross-based cost aggregation (CBCA) during the cost aggregation stage to enhance precision. Winner-take-all (WTA) strategy is implemented to normalise the disparity map values. Finally, the resulting output is subjected to an edge-aware smoothing filter (EASF) to reduce the noise. Due to its resistance to high contrast and brightness, the filter is found to be effective in refining and eliminating noise from the output image. Despite discontinuities like adiron's lost cup handle or artl's shattered rods, this approach, based on experimental research utilizing a Middlebury standard validation benchmark, yields a high level of accuracy, with an average non-occluded error of 6.79%, comparable to other published methods.

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Section: Methodsmentioning

confidence: 99%

Refining disparity maps using deep learning and edge-aware smoothing filter

Abd Gani,

Miskon,

Hamzah

et al. 2024

Bulletin EEI

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show abstract

“…This approach effectively removes noise from outliers while preserving the edges. The median filter (MF) was utilised in [41], [42]. The precision was good around the edges, but MF generates a significant amount of inaccuracy in places with poor texture.…”

Section: Disparity Refinementmentioning

confidence: 99%

Stereo matching algorithm using deep learning and edge-preserving filter for machine vision

Abd Gani,

Miskon,

Hamzah

et al. 2024

Bulletin EEI

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Machine vision research began with a single-camera system, but these systems had various limitations from having just one point-of-view of the environment and no depth information, therefore stereo cameras were invented. This paper proposes a hybrid method of a stereo matching algorithm with the goal of generating an accurate disparity map critical for applications such as 3D surface reconstruction and robot navigation to name a few. Convolutional neural network (CNN) is utilised to generate the matching cost, which is then input into cost aggregation to increase accuracy with the help of a bilateral filter (BF). Winner-take-all (WTA) is used to generate the preliminary disparity map. An edge-preserving filter (EPF) is applied to that output based on a transform that defines an isometry between curves on the 2D image manifold in 5D and the real line to eliminate these artefacts. The transform warps the input signal adaptively to allow linear 1D filtering. Due to the filter's resistance to high contrast and brightness, it is effective in refining and removing noise from the output image. Based on experimental research employing a Middlebury standard validation benchmark, this approach gives high accuracy with an average non-occluded error of 6.71% comparable to other published methods.

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