Abstract
As technologies for image processing, image enhancement can provide more effective information for later data mining and image compression can reduce storage space. In this paper, a smart enhancement scheme during decompression, which combined a novel two-dimensional F-shift (TDFS) transformation and a non-standard two-dimensional wavelet transform (NSTW), is proposed. During the decompression, the first coefficient s00 of the wavelet synopsis was used to adaptively adjust the global gray level of the reconstructed image. Next, the contrast-limited adaptive histogram equalization (CLAHE) was used to achieve the enhancement effect. To avoid a blocking effect, CLAHE was used when the synopsis was decompressed to the second-to-last level. At this time, we only enhanced the low-frequency component and did not change the high-frequency component. Lastly, we used CLAHE again after the image reconstruction. Through experiments, the effectiveness of our scheme was verified. Compared with the existing methods, the compression properties were preserved and the image details and contrast could also be enhanced. The experimental results showed that the image contrast, information entropy, and average gradient were greatly improved compared with the existing methods.
Highlights
For image data, the enhancement processing can provide a higher level of image features for later mining
We present a new method for the decompression by utilizing the F-shift transformation and contrast-limited adaptive histogram equalization (CLAHE)
We developed a smart image enhancement method during decompression and designed a variety of schemes to verify the effectiveness of our method
Summary
The enhancement processing can provide a higher level of image features for later mining. Most image compression methods are based on a transformation domain such that the low-frequency and high-frequency components of the image can be separated. The common feature of the above transformation methods is that they can separate the highfrequency component from the low-frequency component of the image signal. Because the compressed image technology used in this paper is based on an F-shift transformation, it can realize the separation of high- and low-frequency components. On this basis, we present a new method for the decompression by utilizing the F-shift transformation and CLAHE. At the same time, using a CLAHE enhancement at the appropriate time can balance the effect of low-frequency enhancement and high-frequency components on the overall image enhancement.
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