Gamma Correction for Brightness Preservation in Natural Images

Due to improper acquisition settings and other noise artifacts,the image degraded to yield poor mean preservation in brightness.The simplest way to improve the preservation is the implementation of histogram equalization.Because of over-enhancement,it failed to preserve the mean brightness and produce the poor quality of the image.This paper proposes a multi-scale decomposi-tion for brightness preservation using gamma correction.After transformation to hue,saturation and intensity(HSI)channel,the 2D-discrete wavelet transform decomposed the intensity component into low and high-pass coefficients.At the next phase,gamma correction is used by auto-tuning the scale value.The scale is the modified constant value used in the logarithmic function.Further,the scale value is optimized to obtain better visual quality in the image.The optimized value is the weighted distribution of standard deviation-mean of low pass coefficients.Finally,the experimental result is estimated in terms of quality assessment measures used as absolute mean brightness error,the measure of information detail,signal to noise ratio and patch-based contrast quality in the image.By comparison,the proposed method proved to be suitably remarkable in retaining the mean brightness and better visual quality of the image.

Text-to-Sketch Synthesis via Adversarial Network

In the past,sketches were a standard technique used for recognizing offenders and have remained a valuable tool for law enforcement and social security purposes.However,relying on eyewitness observations can lead to discrepancies in the depictions of the sketch,depending on the experience and skills of the sketch artist.With the emergence of modern technologies such as Generative Adversarial Networks(GANs),generating images using verbal and textual cues is now possible,resulting in more accurate sketch depictions.In this study,we propose an adversarial network that generates human facial sketches using such cues provided by an observer.Additionally,we have introduced an Inverse Gamma Correction Technique to improve the training and enhance the quality of the generated sketches.To evaluate the effectiveness of our proposed method,we conducted experiments and analyzed the results using the inception score and Frechet Inception Distance metrics.Our proposed method achieved an overall inception score of 1.438±0.049 and a Frechet Inception Distance of 65.29,outperforming other state-of-the-art techniques.

Constraining Lorentz invariance violation from the continuous spectra of short gamma-ray bursts

In some quantum gravity theories, a foamy structure of space-time may lead to Lorentz invariance violation（LIV）. As the most energetic explosions in the Universe, gamma-ray bursts（GRBs） provide an effect way to probe quantum gravity effects. In this paper, we use the continuous spectra of 20 short GRBs detected by the Swift satellite to give a conservative lower limit of quantum gravity energy scale MQG. Due to the LIV effect, photons with different energy have different velocities. This will lead to the delayed arrival of high energy photons relative to low energy ones. Based on the fact that the LIV-induced time delay cannot be longer than the duration of a GRB,we present the most conservative estimate of the quantum gravity energy scales from 20 short GRBs. The strictest constraint, M_（QG） 〉 5.05 × 10^（14） GeV in the linearly corrected case, is from GRB 140622 A. Our constraint on MQG,although not as tight as previous results, is the safest and most reliable so far.