Traditionally, Image processing applications perform momentous data refinement. One of the most efficacious methods for data manipulation in multiple image processing applications is approximate computing. This mitigates the circuit complexity and thereby reinforces the power, latency, and area metrics. Furthermore, multiplication is also an essential operation in most image-processing applications. In the current scenario, numerous existing state-of-the-art multipliers employed approximation computation techniques to raise the design metrics with limited accuracy. As a consequence, this paper instigates two novel multipliers, namely, one-bit Based Static Segment Inaccurate Multipliers - LBSSIM0 and LBSSIM1, with and without a Hybrid Estimator Logic Circuit (HELC), so as to revamp the accuracy and design metrics. The HELC function is to efface the lower-order significant input bit width data and conceal the inaccurate multiplication of the proposed LBSSIM designs using the barrel shifter and the leading unit. The preferred inaccurate multipliers are simulated and synthesized using Xilinx Vivado, MATLAB, and Cadence RTL compilers for the input widths of 8-bit, 16-bit, and 32-bit. The results reveal that the recommended LBSSIMs dwindle the delay, area, energy, and power on an average of 32.13%, 65.23%, 57.12%, and 64.3%, respectively, with the state-of-the-art Inaccurate Multipliers (IMs). It is also unveiled through the simulation results that the proposed LBSSIMs reinforce the performance of accuracy metrics, namely, MRED, NED, MED, and WCE, on an average of 42.12%, 17.23%, 46.54%, and 28.24%, respectively, as opposed to the state-of-the-art IMs. Eventually, after incorporating the proposed LBSSIMs in the image processing applications, they purveyed higher Peak Signal to Noise Ratio (PSNR) and Structural Similarity Index (SSIM) when collated with the state-of-the-art IMs.