This paper presents a fully automatic method for the separation of diffuse and specular reflection components from a single image. Overall, the mechanisms in which the available methods operate on are computationally costly and do not translate well to modern hardware-implemented image processing pipelines, such as the ones present in consumer electronics. Consequently, the objective of this article is to introduce a simple yet effective method for specular highlight removal. It is based on the dichromatic reflection model and operates through histogram matching in the YCbCr color space. The proposed method performs in real-time. It only uses global image statistics and point-wise intensity transformations. Experimental evaluation shows that the proposed approach has competitive results in comparison to stateof-the-art methods. Limitations of the proposed approach are seldom and are common to most methods available. The proposed method, however, achieves better quality results with much less computational cost, thus enabling feasibility in systems with limited processing power. INDEX TERMS Blind source separation, feature extraction, image color analysis, image enhancement, image processing, image texture analysis.

Summary The Brazilian Environmental Data Collection System (SBCDA) is an asynchronous multiuser message forwarding system based on low earth orbit (LEO) satellites with applications restricted to environmental monitoring and human life protection. This work presents a multiuser SBCDA decoder with low data memory and computational load. The decoder uses spectrum analysis to detect signal presences and upon every signal detection starts a signal decoding process with single‐user bit detection. Simulations considering an Additive White Gaussian Noise (AWGN) scenario with signal's time‐frequency positions uniformly and independently distributed and signal's powers uniformly distributed in dB within a dynamic range of 24 dB are present for different densities of user signals. Those show a frame error rate (FER) of approximately 25% for an average of six coexisting signals at every time instant, in which a missed signal was considered as a frame error. Finally, there are considerations on how to improve the performance maintaining a single‐input and single‐user bit detection strategy.

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