How to remove noise after Otsu Binarization using OpenCV in Python?

Question

I am using a Raspberry Pi and it's Camera to perform some Image Processing algorithm. So , I am performing a background subtraction on successive frames of the captured stream and trying to find if there is any object present in the image and if yes, print out it's area . The algorithm works fine as expected but there is a problem .

The thresholding function which uses cv2.THRESH_OTSU , results into a grainy image whenever there is no object present , i.e the background and the foreground images are same . However those noises/grain disappear when there is an object present in the foreground image . These are as follows -

1. Same Background Image and Foreground Image with noise
2. Different Background and Foreground Image without any noise

As you can see ,if the images are almost same , the noise is present and if any object is introduced in the frame , then the noise vanishes .

I have tried the following to remove the noise but it didn't work .

1. Tried using only cv2.THRESH_BINARY / cv2.THRESH_BINARY_INV without Otsu binariszation.

2. I have tried increasing the brightness/contrast/saturation of the captured image to see if the performance varies , but no change .

3. I have tried to increase/decrease the amount of erosion/dilation preceding the Thresholding step , but this did not make any change either .

This is my code -

from time import sleep
from picamera import PiCamera
from picamera.array import PiRGBArray
import cv2,os
import numpy as np
import threading


def imageSubtract(img):
    bilateral_filtered_image = cv2.bilateralFilter(img, 9, 170, 170)
    bilateral_filtered_image = cv2.cvtColor(bilateral_filtered_image,cv2.COLOR_BGR2GRAY)
    return bilateral_filtered_image

def  imageProcessing():
    camera = PiCamera()
    camera.resolution = (512,512)
    camera.awb_mode="fluorescent"
    camera.iso = 800
    camera.contrast=33
    camera.brightness=75
    camera.sharpness=100
    rawCapture = PiRGBArray(camera, size=(512, 512))

    first_time=0
    frame_buffer=0
    counter=0
    camera.start_preview()
    sleep(2)


    for frame in camera.capture_continuous(rawCapture, format="bgr", use_video_port=True):

        if first_time==0:
            rawCapture.truncate(0)
            if frame_buffer<10:
               print("Frame rejected -",str(frame_buffer))
               frame_buffer+=1
               continue
            os.system("clear")
            refImg=frame.array
            refThresh=imageSubtract(refImg)
            first_time=1



        image = frame.array
        cv2.imshow("Foreground", image)
        key = cv2.waitKey(1)
        rawCapture.truncate(0)
        newThresh=imageSubtract(image)

        diff=cv2.absdiff(refThresh,newThresh)
        kernel = np.ones((5,5),np.uint8)

        diff=cv2.dilate(diff,kernel,iterations = 3)
        cv2.imshow("Background",refImg)
        _, thresholded = cv2.threshold(diff, 0 , 255, cv2.THRESH_BINARY +cv2.THRESH_OTSU)


        _, contours, _= cv2.findContours(thresholded,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
        try:
           c=max(contours,key=cv2.contourArea)
           x,y,w,h = cv2.boundingRect(c)
           cv2.rectangle(thresholded,(x,y),(x+w,y+h),(125,125,125),2)
           if cv2.contourArea(c)>500:
              print("Object detected with area = ",cv2.contourArea(c))
           cv2.imshow("Threshold",thresholded)
           if key == ord('q'):
               camera.close()
               cv2.destroyAllWindows()
               break
        except Exception as e:
           pass

if __name__ == "__main__" :
   imageProcessing()

Please help me to remove the noise when the background and foreground Images are same .

Thank You !